Data-driven bus timetabling with spatial-temporal travel time

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

The role of key online reviews in affecting online hotel booking: an empirical investigation

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

Low-carbon VRP for cold chain logistics considering real-time traffic conditions in the road network

PurposeThis paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.Design/methodology/approachThis study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.FindingsThe result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.Originality/valueExisting research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.

Understanding Ant Forest continuance: effects of user experience, personal attributes and motivational factors

PurposeTechnology has emerged as a leading tool to address concerns regarding climate change in the recent era. As a result, the green mobile application – Ant Forest – was developed, and it has considerable potential to reduce negative environmental impacts by encouraging its users to become involved in eco-friendly activities. Ant Forest is a novel unexplored green mobile gaming phenomenon. To address this gap, this study explores the influence of user experience (cognitive experience and affective experience), personal attributes (affection and altruism) and motivational factors in game play (reward for activities and self-promotion) on the continuation intention toward Ant Forest.Design/methodology/approachThe authors assessed the data using partial least squares structural equation modeling (PLS-SEM) for understanding users' continuation intention toward Ant Forest.FindingsThrough a survey of 337 Ant Forest users, the results reveal that cognitive and affective experiences substantially affect Ant Forest continuation intention. Personal attributes and motivational factors also stimulate users to continue using Ant Forest.Originality/valueThe authors build and confirm a conceptual framework to understand users' continuation intention toward a novel unexplored Ant Forest phenomenon.

Analytical target cascading for multi-level supply chain decisions in cloud perspective

PurposeThe purpose of this paper is to adopt a three-stage cloud-based management system for optimizing greenhouse gases (GHG) emission and marketing decisions with supplier selection and product family design in a multi-level supply chain with multiple suppliers, one single manufacturer and multiple retailers.Design/methodology/approachThe manufacturer purchases optional components of a certain functionality from his alternative suppliers and customizes a set of platform products for retailers in different independent market segments. To tackle the studied problem, a hierarchical analytical target cascading (ATC) model is proposed, Jaya algorithm is applied and supplier selection and product family design are implemented in its encoding procedure.FindingsA case study is used to verify the effectiveness of the ATC model in solving the optimization problem and the corresponding algorithm. It has shown that the ATC model can not only obtain close optimization results as a central optimization method but also maintain the autonomous decision rights of different supply chain members.Originality/valueThis paper first develops a three-stage cloud-based management system to optimize GHG emission, marketing decisions, supplier selection and product family design in a multi-level supply chain. Then, the ATC model is proposed to obtain the close optimization results as central optimization method and also maintain the autonomous decision rights of different supply chain members.

Environmental efficiency of Xiangjiang River in China: a data envelopment analysis cross-efficiency approach

PurposeEnvironmental performance becomes a key issue for the sustainable development. Recently, incremental information technology is adopted to collect environmental data and improve environmental performance. Previous environmental efficiency measures mainly focus on individual decision-making units (DMUs). Benefited from the information technology, this paper develops a new environmental efficiency measure to explore the implicit alliances among DMUs and applies it to Xiangjiang River.Design/methodology/approachThis study formulates a new data envelopment analysis (DEA) environmental cross-efficiency measure that considers DMUs' alliances. Each DMUs' alliance is formulated by the DMUs who are supervised by the same manager. In cross-efficiency evaluation context, this paper adopts DMUs' alliances rather than individual DMUs to derive the environmental cross-efficiency measure considering undesirable outputs. Furthermore, the Tobit regression is conducted to analyze the influence of exogenous factors about the environmental cross-efficiency.FindingsThe findings show that (1) Chenzhou performs the best while Xiangtan performed the worst along Xiangjiang River. (2) The environmental efficiency of cities in Xiangjiang River is generally low. Increasing public budgetary expenditure can improve environmental efficiency of cities. (3) The larger the alliance size, the higher environmental efficiency. (4) The income level is negatively correlated with environmental efficiency, indicating that the economy is at the expense of the environment in Xiangjiang River.Originality/valueThis paper contributes to developing a new environmental DEA cross-efficiency measure considering DMUs' alliance, and combining DEA cross-efficiency and Tobit regression in environmental performance measurement of Xiangjiang River. This paper examines the exogenous factors that have influences on environmental efficiency of Xiangjiang River and derive policy implications to improve the sustainable operation.

Artificial intelligence in safety-critical systems: a systematic review

PurposeThis study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.Design/methodology/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.Practical implicationsThis study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.Originality/valueThis is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.

The effects of socio-technical integration on sustainability practices: a supply chain perspective

PurposeThis study aims to investigate sustainable supply chain management (SSCM) through the lens of socio-technical system (STS) theory. Specifically, it examines the individual and synergistic effects of social and technical integration on two main sustainability practices (i.e. sustainable production and sustainable sourcing). Supply chain uncertainty is further explored as a key environmental factor.Design/methodology/approachA moderated joint effects model was hypothesized. A sample of 759 manufacturing firms was used to test the proposed hypotheses by hierarchical linear regression.FindingsThe results show that both social and technical integration have positive effects on sustainable production and sustainable sourcing. Interestingly, social and technical integration have an enhancing synergistic effect on sustainable sourcing, which is further strengthened in high-uncertainty supply chains.Originality/valueThis study extends the application of STS theory in the SSCM setting. It enriches the sustainability literature by uncovering the impact of the interplay among the firm's social, technical and environmental systems on sustainable production and sourcing, and offers system-wide insights for sustainability management.

How does information integration enhance SMEs’ credit quality: the mediating role of supply chain capabilities

PurposeThis paper aims to investigate the relationship between information integration, supply chain capabilities and credit quality of small and middle enterprises (SMEs) in supply chain finance (SCF).Design/methodology/approachGrounded in the resource-based view (RBV) and signaling theory, this study proposes a theoretical model. Then, structural equation modeling and interview analysis are employed to test the theoretical model.FindingsThe results show that both two aspects of information integration, namely, information technology and information sharing, have positive effects on the SMEs’ credit quality in SCF, and these effects are mediated by supply chain capabilities.Originality/valueFirst, the paper contributes to SCF literature by simultaneously examining the role of two dimensions of information integration (information technology and information sharing) in enhancing SMEs’ credit quality. Second, this paper enriches the existing theoretical research on SCF by integrating the SMEs perspective and SCF service provider perspective. Moreover, this paper explores the indirect effects of information integration on SMEs’ credit quality by incorporating supply chain capabilities as a mediating factor.

The dynamics of cryptocurrency market behavior: sentiment analysis using Markov chains

PurposeThe authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.Design/methodology/approachThe authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.FindingsThe authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.Originality/valueThe proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.